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DTSTAMP:20210402T160554Z
LOCATION:Track 4
DTSTART;TZID=America/New_York:20201111T101000
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UID:submissions.supercomputing.org_SC20_sess201_pec220@linklings.com
SUMMARY:IA^3 2020 – Keynote: Research Challenges in Compiler Technology fo
 r Sparse Tensors
DESCRIPTION:Workshop\n\nIA^3 2020 – Keynote: Research Challenges in Compil
 er Technology for Sparse Tensors\n\nHall\n\nScalable computations where th
 e data is sparse — that is, a tiny subset of the data is populated — are w
 idely represented in scientific computing, data analytics and machine lear
 ning.  Sparse data are typically represented by sparse matrices and graphs
 , which reduce data storage and computation requirements (e.g., by storing
  only nonzero data elements) through the use of indirection matrices such 
 as B in A[B[i]].  This indirection impacts performance on modern architect
 ures.  Whether the code is optimized by human programmer, compiler or hard
 ware, any optimizations that must understand the memory access patterns fo
 r A require the values of B, which are not known until runtime.  Generatin
 g high-performance code for sparse computations is made more challenging b
 y modern architectures, where data movement cost is dominant.  Significant
  interest in compiler technology for sparse tensors has arisen with the in
 tense interest in machine learning systems.  In this talk, we will approac
 h this challenge from a compiler perspective, and describe active areas of
  research and future requirements.\n\nRegistration Category: Workshop Reg 
 Pass
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